It looks like the prediction models are the unfitted version.
Traceback (most recent call last): File "main.py", line 71, in <module> main() File "main.py", line 55, in main summary = biz.aspect_based_summary() File "/Users/yul0b/Documents/YULU/Workspace/research/ranking/data/opinion-mining-master/classes/business.py", line 93, in aspect_based_summary asp_dict = dict([(aspect, self.aspect_summary(aspect)) for aspect in aspects]) File "/Users/yul0b/Documents/YULU/Workspace/research/ranking/data/opinion-mining-master/classes/business.py", line 184, in aspect_summary prob_opin = Business.OPINION_MODEL.get_opinionated_proba(sent) File "/Users/yul0b/Documents/YULU/Workspace/research/ranking/data/opinion-mining-master/classes/transformers/sentiment.py", line 15, in get_opinionated_proba return OpinionModel.OPINION_MODEL.predict_proba(sent.get_features(asarray=True))[0][1] File "/Library/Python/2.7/site-packages/sklearn/utils/metaestimators.py", line 54, in <lambda> out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) File "/Library/Python/2.7/site-packages/sklearn/pipeline.py", line 379, in predict_proba Xt = transform.transform(Xt) File "/Library/Python/2.7/site-packages/sklearn/preprocessing/data.py", line 641, in transform check_is_fitted(self, 'scale_') File "/Library/Python/2.7/site-packages/sklearn/utils/validation.py", line 690, in check_is_fitted raise _NotFittedError(msg % {'name': type(estimator).__name__}) sklearn.exceptions.NotFittedError: This StandardScaler instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.
It looks like the prediction models are the unfitted version.
Traceback (most recent call last): File "main.py", line 71, in <module> main() File "main.py", line 55, in main summary = biz.aspect_based_summary() File "/Users/yul0b/Documents/YULU/Workspace/research/ranking/data/opinion-mining-master/classes/business.py", line 93, in aspect_based_summary asp_dict = dict([(aspect, self.aspect_summary(aspect)) for aspect in aspects]) File "/Users/yul0b/Documents/YULU/Workspace/research/ranking/data/opinion-mining-master/classes/business.py", line 184, in aspect_summary prob_opin = Business.OPINION_MODEL.get_opinionated_proba(sent) File "/Users/yul0b/Documents/YULU/Workspace/research/ranking/data/opinion-mining-master/classes/transformers/sentiment.py", line 15, in get_opinionated_proba return OpinionModel.OPINION_MODEL.predict_proba(sent.get_features(asarray=True))[0][1] File "/Library/Python/2.7/site-packages/sklearn/utils/metaestimators.py", line 54, in <lambda> out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) File "/Library/Python/2.7/site-packages/sklearn/pipeline.py", line 379, in predict_proba Xt = transform.transform(Xt) File "/Library/Python/2.7/site-packages/sklearn/preprocessing/data.py", line 641, in transform check_is_fitted(self, 'scale_') File "/Library/Python/2.7/site-packages/sklearn/utils/validation.py", line 690, in check_is_fitted raise _NotFittedError(msg % {'name': type(estimator).__name__}) sklearn.exceptions.NotFittedError: This StandardScaler instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.